To further enhance the joint representation learning, we add an autoencoder [1]} reconstruction loss between the original teachers' feature space. Let \({F^{\prime }_{T_i}}\)  denote the reconstructed feature of teacher \(T_i\) , the reconstruction loss \(\mathcal {L}_R\)  is defined as
\(\mathcal {L}_R = \sum _{i=1}^N || F^{\prime }_{T_i} - F_{T_i}||_2,\) 
